Update MPIUtil to singleton

CHANGELOG.md

@@ -1,3 +1,8 @@
+## Version: 0.5.5
+### Date: 2018-10-25
+1. Make MPIUtil a singleton and remove in-module instantiation.
+2. Standardize docstring for Sphinx documentation
+
 ## Version: 0.5.4
 ### Date: 2018-01-30
 1. Fixed important bugs in individual clone.

gaft/analysis/console_output.py

@@ -5,6 +5,16 @@
 
 
 class ConsoleOutput(OnTheFlyAnalysis):
+    ''' Built-in on-the-fly analysis plugin class for outputing log on console.
+
+    Attribute:
+
+        interval(:obj:`int`): The analysis interval in evolution iteration, default 
+                              value is 1 meaning analyze every step.
+
+        master_only(:obj:`bool`): Flag for if the analysis plugin is only effective 
+                                  in master process. Default is True.
+    '''
 
     # Analysis interval.
     interval = 1

gaft/analysis/fitness_store.py

@@ -4,6 +4,14 @@
 from ..plugin_interfaces.analysis import OnTheFlyAnalysis
 
 class FitnessStore(OnTheFlyAnalysis):
+    ''' Built-in on-the-fly analysis plugin class for storing fitness related data during iteration.
+
+    Attribute:
+        interval(:obj:`int`): The analysis interval in evolution iteration, default 
+                              value is 1 meaning analyze every step.
+        master_only(:obj:`bool`): Flag for if the analysis plugin is only effective 
+                                  in master process. Default is True.
+    '''
 
     # Analysis interval.
     interval = 1

gaft/operators/crossover/__init__.py

@@ -1,2 +1,4 @@
 ''' Package for built-in crossover operators '''
 
+from .uniform_crossover import UniformCrossover
+

gaft/operators/crossover/uniform_crossover.py

@@ -10,16 +10,16 @@
 
 
 class UniformCrossover(Crossover):
-    def __init__(self, pc, pe=0.5):
-        '''
-        Crossover operator with uniform crossover algorithm,
-        see https://en.wikipedia.org/wiki/Crossover_(genetic_algorithm)
+    ''' Crossover operator with uniform crossover algorithm,
+    see https://en.wikipedia.org/wiki/Crossover_(genetic_algorithm)
 
-        :param pc: The probability of crossover (usaully between 0.25 ~ 1.0)
-        :type pc: float in (0.0, 1.0]
+    :param pc: The probability of crossover (usaully between 0.25 ~ 1.0)
+    :type pc: float in (0.0, 1.0]
 
-        :param pe: Gene exchange probability.
-        '''
+    :param pe: Gene exchange probability.
+    :type pe: float in range (0.0, 1.0]
+    '''
+    def __init__(self, pc, pe=0.5):
         if pc <= 0.0 or pc > 1.0:
             raise ValueError('Invalid crossover probability')
         self.pc = pc
@@ -29,8 +29,13 @@ def __init__(self, pc, pe=0.5):
         self.pe = pe
 
     def cross(self, father, mother):
-        '''
-        Cross chromsomes of parent using uniform crossover method.
+        ''' Cross chromsomes of parent using uniform crossover method.
+
+        :param population: Population where the selection operation occurs.
+        :type population: :obj:`gaft.components.Population`
+
+        :return: Selected parents (a father and a mother)
+        :rtype: list of :obj:`gaft.components.IndividualBase`
         '''
         do_cross = True if random() <= self.pc else False
 

gaft/operators/mutation/__init__.py

@@ -1,3 +1,5 @@
 
 ''' Package for built-in mutation operators '''
 
+from .flip_bit_mutation import FlipBitMutation, FlipBitBigMutation
+

gaft/operators/mutation/flip_bit_mutation.py

@@ -14,21 +14,28 @@
 
 
 class FlipBitMutation(Mutation):
-    def __init__(self, pm):
-        '''
-        Mutation operator with Flip Bit mutation implementation.
+    ''' Mutation operator with Flip Bit mutation implementation.
 
-        :param pm: The probability of mutation (usually between 0.001 ~ 0.1)
-        :type pm: float in (0.0, 1.0]
-        '''
+    :param pm: The probability of mutation (usually between 0.001 ~ 0.1)
+    :type pm: float in range (0.0, 1.0]
+    '''
+    def __init__(self, pm):
         if pm <= 0.0 or pm > 1.0:
             raise ValueError('Invalid mutation probability')
 
         self.pm = pm
 
     def mutate(self, individual, engine):
-        '''
-        Mutate the individual.
+        ''' Mutate the individual.
+
+        :param individual: The individual on which crossover operation occurs
+        :type individual: :obj:`gaft.components.IndividualBase`
+
+        :param engine: Current genetic algorithm engine
+        :type engine: :obj:`gaft.engine.GAEngine`
+
+        :return: A mutated individual
+        :rtype: :obj:`gaft.components.IndividualBase`
         '''
         do_mutation = True if random() <= self.pm else False
 
@@ -56,21 +63,20 @@ def mutate(self, individual, engine):
 
 
 class FlipBitBigMutation(FlipBitMutation):
-    def __init__(self, pm, pbm, alpha):
-        '''
-        Mutation operator using Flip Bit mutation implementation with adaptive
-        big mutation rate to overcome premature or local-best solution.
+    ''' Mutation operator using Flip Bit mutation implementation with adaptive
+    big mutation rate to overcome premature or local-best solution.
 
-        :param pm: The probability of mutation (usually between 0.001 ~ 0.1)
-        :type pm: float in (0.0, 1.0]
+    :param pm: The probability of mutation (usually between 0.001 ~ 0.1)
+    :type pm: float in (0.0, 1.0]
 
-        :param pbm: The probability of big mutation, usually more than 5 times
-                    bigger than pm.
-        :type pbm: float
+    :param pbm: The probability of big mutation, usually more than 5 times
+                bigger than pm.
+    :type pbm: float
 
-        :param alpha: intensive factor
-        :type alpha: float, in range (0.5, 1)
-        '''
+    :param alpha: intensive factor
+    :type alpha: float, in range (0.5, 1)
+    '''
+    def __init__(self, pm, pbm, alpha):
         super(self.__class__, self).__init__(pm)
 
         if not (0.0 < pbm < 1.0):
@@ -85,8 +91,16 @@ def __init__(self, pm, pbm, alpha):
         self.alpha = alpha
 
     def mutate(self, individual, engine):
-        '''
-        Mutate the individual with adaptive big mutation rate.
+        ''' Mutate the individual with adaptive big mutation rate.
+
+        :param individual: The individual on which crossover operation occurs
+        :type individual: :obj:`gaft.components.IndividualBase`
+
+        :param engine: Current genetic algorithm engine
+        :type engine: :obj:`gaft.engine.GAEngine`
+
+        :return: A mutated individual
+        :rtype: :obj:`gaft.components.IndividualBase`
         '''
         pm = self.pm
 

gaft/operators/selection/__init__.py

@@ -1,2 +1,7 @@
 ''' Package for built-in selection operators '''
 
+from .exponential_ranking_selection import ExponentialRankingSelection
+from .linear_ranking_selection import LinearRankingSelection
+from .roulette_wheel_selection import RouletteWheelSelection
+from .tournament_selection import TournamentSelection
+

gaft/operators/selection/exponential_ranking_selection.py

@@ -11,21 +11,25 @@
 
 
 class ExponentialRankingSelection(Selection): 
-    def __init__(self, base=0.5):
-        '''
-        Selection operator using Exponential Ranking selection method.
+    ''' Selection operator using Exponential Ranking selection method.
 
-        :param base: The base of exponent
-        :type base: float in range (0.0, 1.0)
-        '''
+    :param base: The base of exponent
+    :type base: float in range (0.0, 1.0)
+    '''
+    def __init__(self, base=0.5):
         if not (0.0 < base < 1.0):
             raise ValueError('The base of exponent c must in range (0.0, 1.0)')
 
         self.base = base
 
     def select(self, population, fitness):
-        '''
-        Select a pair of parent individuals using exponential ranking method.
+        ''' Select a pair of parent individuals using exponential ranking method.
+
+        :param population: Population where the selection operation occurs.
+        :type population: :obj:`gaft.components.Population`
+
+        :return: Selected parents (a father and a mother)
+        :rtype: list of :obj:`gaft.components.IndividualBase`
         '''
         # Individual number.
         NP = len(population)

gaft/operators/selection/linear_ranking_selection.py

@@ -11,20 +11,24 @@
 
 
 class LinearRankingSelection(Selection):
-    def __init__(self, pmin=0.1, pmax=0.9):
-        '''
-        Selection operator using Linear Ranking selection method.
+    ''' Selection operator using Linear Ranking selection method.
 
-        Reference: Baker J E. Adaptive selection methods for genetic
-        algorithms[C]//Proceedings of an International Conference on Genetic
-        Algorithms and their applications. 1985: 101-111.
-        '''
+    Reference: Baker J E. Adaptive selection methods for genetic
+    algorithms[C]//Proceedings of an International Conference on Genetic
+    Algorithms and their applications. 1985: 101-111.
+    '''
+    def __init__(self, pmin=0.1, pmax=0.9):
         # Selection probabilities for the worst and best individuals.
         self.pmin, self.pmax = pmin, pmax
 
     def select(self, population, fitness):
-        '''
-        Select a pair of parent individuals using linear ranking method.
+        ''' Select a pair of parent individuals using linear ranking method.
+
+        :param population: Population where the selection operation occurs.
+        :type population: :obj:`gaft.components.Population`
+
+        :return: Selected parents (a father and a mother)
+        :rtype: list of :obj:`gaft.components.IndividualBase`
         '''
         # Individual number.
         NP = len(population)

gaft/operators/selection/roulette_wheel_selection.py

@@ -10,16 +10,20 @@
 from ...plugin_interfaces.operators.selection import Selection
 
 class RouletteWheelSelection(Selection):
+    ''' Selection operator with fitness proportionate selection(FPS) or
+    so-called roulette-wheel selection implementation.
+    '''
     def __init__(self):
-        '''
-        Selection operator with fitness proportionate selection(FPS) or
-        so-called roulette-wheel selection implementation.
-        '''
         pass
 
     def select(self, population, fitness):
-        '''
-        Select a pair of parent using FPS algorithm.
+        ''' Select a pair of parent using FPS algorithm.
+
+        :param population: Population where the selection operation occurs.
+        :type population: :obj:`gaft.components.Population`
+
+        :return: Selected parents (a father and a mother)
+        :rtype: list of :obj:`gaft.components.IndividualBase`
         '''
         # Normalize fitness values for all individuals.
         fit = population.all_fits(fitness)

gaft/operators/selection/tournament_selection.py

@@ -9,16 +9,23 @@
 
 
 class TournamentSelection(Selection):
+    ''' Selection operator using Tournament Strategy with tournament size equals
+    to two by default.
+
+    :param tournament_size: Individual number in one tournament
+    :type tournament_size: int
+    '''
     def __init__(self, tournament_size=2):
-        '''
-        Selection operator using Tournament Strategy with tournament size equals
-        to two by default.
-        '''
         self.tournament_size = tournament_size
 
     def select(self, population, fitness):
-        '''
-        Select a pair of parent using Tournament strategy.
+        ''' Select a pair of parent using Tournament strategy.
+
+        :param population: Population where the selection operation occurs.
+        :type population: :obj:`gaft.components.Population`
+
+        :return: Selected parents (a father and a mother)
+        :rtype: list of :obj:`gaft.components.IndividualBase`
         '''
         # Competition function.
         all_fits = population.all_fits(fitness)

gaft/plugin_interfaces/analysis.py

@@ -5,9 +5,16 @@
 
 
 class OnTheFlyAnalysis(metaclass=AnalysisMeta):
-    '''
-    Class for providing an interface to easily extend and customize the behavior
+    ''' Class for providing an interface to easily extend and customize the behavior
     of the on-the-fly analysis functionality of gapy.
+
+    Attribute:
+
+        interval(:obj:`int`): The analysis interval in evolution iteration, default 
+                              value is 1 meaning analyze every step.
+
+        master_only(:obj:`bool`): Flag for if the analysis plugin is only effective 
+                                  in master process. Default is True.
     '''
     # Only used in master process?
     master_only = False
@@ -16,15 +23,14 @@ class OnTheFlyAnalysis(metaclass=AnalysisMeta):
     interval = 1
 
     def setup(self, ng, engine):
-        '''
-        Function called right before the start of genetic algorithm main iteration
+        ''' Function called right before the start of genetic algorithm main iteration
         to allow for custom setup of the analysis object.
 
         :param ng: The number of generation.
         :type ng: int
 
         :param engine: The current GAEngine where the analysis is running.
-        :type engine: GAEngine
+        :type engine: gaft.engine.GAEngine
         '''
         raise NotImplementedError
 
@@ -39,7 +45,7 @@ def register_step(self, g, population, engine):
         :type population: Population
 
         :param engine: The current GAEngine where the analysis is running.
-        :type engine: GAEngine
+        :type engine: gaft.engine.GAEngine
         '''
         raise NotImplementedError
 
@@ -52,7 +58,7 @@ def finalize(self, population, engine):
         :type population: Population
 
         :param engine: The current GAEngine where the analysis is running.
-        :type engine: GAEngine
+        :type engine: gaft.engine.GAEngine
         '''
         raise NotImplementedError